Improving Lifetime of WSNs Using Energy-Efficient Information Gathering Algorithms and Magnetic Resonance
Manish Bhardwaj*, Jinee Kumar
Computer Science and Engineering Department, SRM University, Ghaziabad, India
To cite this article:
Manish Bhardwaj, Jinee Kumar. Improving Lifetime of WSNs Using Energy-Efficient Information Gathering Algorithms and Magnetic Resonance.Advances in Wireless Communications and Networks.Vol.1, No. 2, 2015, pp. 11-16. doi: 10.11648/j.awcn.20150102.11
Abstract: This paper introduce the concept of the energy-efficient Information gathering algorithms for improving lifetime of WSNs and wirelessly recharge the sensor nodes. Here we have assumed that the sensor nodes and base-station are not mobile. The more over location and initial energy of the sensor nodes is known and number of sensor nodes is randomly distributed over a monitoring region. For the heterogeneity the three types of nodes: a normal, advanced and super node with some fraction in terms of their initial energy has been taken. In this work, we have proposed new distributed energy efficient algorithms PEIPSH and ILBPSH, based on the distance from the base station and sensor residual energy as well as scheduling of sensor nodes to alternate between sleep and active mode. The simulation results shows that the proposed algorithms PEIPSH and ILBPSH balance the energy dissipation over the whole network and improve the network lifetime.
Keywords: Wireless Sensor Networks, Targets Coverage, Adjustable Sensing Range, Heterogeneity, Maximize Lifetime
A wireless sensor network (WSN) is defined as a network of (possibly low-size, low-battery power and low-complex) devices denoted as nodes that can sense the environment and communicate the information gathered from the monitored field (such as an area or volume) through wireless links; the sense data is forwarded, possibly via multiple hops relaying, to a sink (controller or monitor ) that can use it locally, or is connected to other networks (e.g., the Internet) through a gateway. A node in sensor network consists of CPU, memory, battery and transceiver. The size of each sensor node varies with application . The nodes can be stationary or moving. They can be location-aware or not. They can be homogeneous or heterogeneous. Sensor networks can be classified into different ways. One way is whether the nodes are individually addressable and another is the data in the network are aggregated. Whether addressability is needed depends on the application. In flat networks, each node normally takes the similar role and sensor nodes work together to perform the sensing task. Due to the huge number of sensor nodes, it is not possible to allocate a overall identifier to each node. This deliberation has led to data centric routing, where the BS sends queries to certain regions and waits for information from the sensors positioned in the selected regions. While data is being requested through queries, attribute based naming is necessary to identify the properties of data. Some of routing protocols in this kind are: SPIN , Directed Diffusion .
Hierarchical or cluster-based routing, are recognized techniques with special compensation related to efficient communication, scalability and have been utilized to perform energy-efficient routing in WSNs. In a cluster-based architecture, higher energy nodes can be used to procedure and send the information whereas low energy nodes can be used to perform the sensing in the nearness of the target. Some of routing protocols in this group are: LEACH , PEGASIS .
In this paper, we propose two energy efficient hierarchical information collecting algorithms for heterogeneous sensor networks. Algorithms include two phases: the cluster head arrangement phase and the routing phase. For the cluster head arrangement, algorithms adopt the head node on the basis of the distance (how far the Base-station is located from the head node) and its energy level. After the cluster head arrangement phase, algorithms constructs a routing tree over the set of head nodes but only the higher residual energy nodes can communicate with the Base station by single-hop communication. The remainder of the paper is prepared as follows: In Section 2 some related work is presented. In Section 3, brief of wireless electricity concept. In section 4, the network radio model for energy calculations and problem statement has been discussed. In Section 5, the details of centralized algorithms for SNLP and its simulation have been provided. We present results and discussion in Section 6. Section 7 concludes the paper.
2. Litreture Survey
Heinzelman et al.  propose LEACH, a substitute clustering based algorithm. In order to save energy, LEACH deals with the heterogeneous energy condition is the node with higher energy should have larger probability of becoming the cluster head. Each sensor node must have an approximation of the total energy of all nodes in the network to compute the probability of becoming a cluster head but it cannot make decision of becoming a cluster head only by its local information, so the scalability of this scheme will be influenced. Sh. Lee et al. suggest a new clustering algorithm CODA  in order to mitigate the unbalance of energy depletion caused by different distance from the sink. CODA divides the whole network into a small number of groups based on the distance from the base station and the strategy of routing and each group has its own number of cluster members and member nodes. The farther the distance from the base station, the more clusters are formed in case of single hop with clustering. It shows better performance than applying the same probability to the whole network in terms of the network lifetime and the dissipated energy.
In  authors report an algorithm based on chain, which uses greedy algorithm to form data chain. Each node, aggregates data from downstream node and sends it to upstream node along the chain and communicates only with a close neighbor and takes turns transmitting to the base station, thus reducing the amount of energy spent per round. In , the authors discuss a HEED clustering algorithm which periodically selects cluster head based on the node residual energy and node degree and a secondary parameter, such as node proximity to its neighbors or node degree. The clustering process terminates in O(1) iterations and it also achieves fairly uniform cluster head distribution across the network and selection of the secondary clustering parameter can balance load among cluster heads.
In  the authors introduce a cluster head election method using fuzz logic to overcome the defects of LEACH. They inquired that the network lifetime can be prolonged by using fuzz variables in homogeneous network system, which is different from the heterogeneous energy consideration.
In  author introduce the concept of cooperative communication so that same data can be sends by several nodes simultaneously. This paper proposed optimum relay nodes selection for CC network to reduce overall power consumption of network.
In  this paper using a Witricity and Backpressure Technique. The simulation results show that the proposed algorithm is able to find a better solution, fast convergence speed and high reliability. This Paper proposed scheme is useful for minimizing the overheads, maintaining the route reliability and improving the link utilization.
3. Concept of Wirelessly Power Transformation
Household devices produce relatively small magnetic fields. For this reason, chargers hold devices at the distance necessary to induce a current, which can only happen if the coils are close together. A larger, stronger field could induce current from farther away, but the process would be extremely inefficient. Since a magnetic field spreads in all directions, making a larger one would waste a lot of energy. An efficient way to transfer power between coils separated by a few meters is that we could extend the distance between the coils by adding resonance to the equation. A good way to understand resonance is to think of it in terms of sound. An object's physical structure -- like the size and shape of a trumpet -- determines the frequency at which it naturally vibrates. This is its resonant frequency . It's easy to get objects to vibrate at their resonant frequency and difficult to get them to vibrate at other frequencies. This is why playing a trumpet can cause a nearby trumpet to begin to vibrate. Both trumpets have the same resonant frequency. Induction can take place little differently if the electromagnetic fields around the coils resonate at the same frequency. The theory uses a curved coil of wire as an inductor. A capacitance plate, which can hold a charge, attaches to each end of the coil. As electricity travels through this coil, the coil begins to resonate. Its resonant frequency is a product of the inductance of the coil and the capacitance of the plates .
Electricity, traveling along an electromagnetic wave, can tunnel from one coil to the other as long as they both have the same resonant frequency. In a short theoretical analysis they demonstrate that by sending electromagnetic waves around in a highly angular waveguide, evanescent waves are produced which carry no energy. An evanescent wave is near field standing wave exhibiting exponential decay with distance. If a proper resonant waveguide is brought near the transmitter, the evanescent waves can allow the energy to tunnel (specifically evanescent wave coupling, the electromagnetic equivalent of tunneling to the power drawing waveguide, where they can be rectified into DC power. Since the electromagnetic waves would tunnel, they would not propagate through the air to be absorbed or dissipated, and would not disrupt electronic devices. As long as both coils are out of range of one another, nothing will happen, since the fields around the coils aren't strong enough to affect much around them. Similarly, if the two coils resonate at different frequencies, nothing will happen. But if two resonating coils with the same frequency get within a few meters of each other, streams of energy move from the transmitting coil to the receiving coil. According to the theory, one coil can even send electricity to several receiving coils, as long as they all resonate at the same frequency. The researchers have named this non-radiative energy transfer since it involves stationary fields around the coils rather than fields that spread in all directions.
According to the theory, one coil can recharge any device that is in range, as long as the coils have the same resonant frequency. "Resonant inductive coupling" has key implications in solving the two main problems associated with nonresonant inductive coupling and electromagnetic radiation, one of which is caused by the other; distance and efficiency. Electromagnetic induction works on the principle of a primary coil generating a predominantly magnetic field and a secondary coil being within that field so a current is induced within its coils. This causes the relatively short range due to the amount of power required to produce an electromagnetic field. Over greater distances the non-resonant induction method is inefficient and wastes much of the transmitted energy just to increase range. This is where the resonance comes in and helps efficiency dramatically by "tunneling" the magnetic field to a receiver coil that resonates at the same frequency. Unlike the multiple-layer secondary of a non-resonant transformer, such receiving coils are single layer solenoids with closely spaced capacitor plates on each end, which in combination allow the coil to be tuned to the transmitter frequency thereby eliminating the wide energy wasting "wave problem" and allowing the energy used to focus in on a specific frequency increasing the range.
4. Model for Wireless Sensor Networks
In this section, we define the network model and wireless radio model which is used during the simulation of the protocols.
4.1. Network Model
Assume n sensor nodes are randomly and uniformly distributed over the sensing field R and the sensor network has the following properties:
1. This network is a static compactly deployed network. It means n sensor nodes are compactly deployed in a two dimensional geographic space, forming a network and those nodes do not move any more after deployment.
2. All nodes should be approximately time coordinated on the order of seconds.
3. There is one base station, which is deployed at (75, 75) position.
4. Nodes are location-aware, i.e. not equipped with GPS capable antennae.
5. There are three types of nodes normal, advance and super nodes. Advance and super nodes are equipped with more battery energy than normal node.
6. These nodes are uniformly distributed over the region R and they are not mobile.
4.2. Wireless Radio Model
We have used similar wireless radio dissipation model as proposed in  and illustrated in figure. 3 According to the radio dissipation model, The Signal-to-Noise Ratio (SNR) in transmitting an L bit message over a distance d, energy expanded by the radio is given by (1) and to receive this message, the radio expends energy as (2):
ETX = Eelec*L εamp *L*dn (1)
Where Eelec is the energy dissipated per bit to run the transmitter or the receiver circuit, εfs and depend εmp on the transmitter amplifier model used, and d is the distance between the sender and the receiver. By equating the two expressions at d=d0, we have d0 = √ εfs/ εmp To receive an L bit message the radio expends
|Energy consumed by theamplifier to transmit at ashorter distance||εfs||10nJ/bit/m2|
|Energy consumed by theamplifier to transmit at alonger distance||εmp||0.0013pJ/bit/m4|
|Energy consumed in theelectronics circuit to transmitor receive the signal||Eelec||50 nJ/bit|
|Energy for data aggregation||EDA||5 nJ/bit/Signal|
5. Centralized Algorithms forSnlp and Its Simulation
5.1. Explanation of Proposed Algorithms
In this algorithm, decision of sensor head and states depends on both the energy level of each sensor and distance (between sensor to neighbor’s sensor and sensors to base station). The algorithm has the following steps:
Step:-1.The location of base station fixed at (75, 75) and sensors are read from the input file. It contains the information of sensors x, y position, sensors id and set the initial energy value for each sensor node.
Step:-2.Sensor nodes networks are divided into three categories of the sensor such as advance nodes, super nodes and normal nodes. These sensor nodes used through a heterogeneity model that directly impact on the battery power of sensor nodes.
Step:-3. At any consequence, each sensor stays in one state out of the three states.
A. Active State: the sensor monitors the area, collect the information from the monitoring field and send to the base station.
B. Idle State: idle and sleep modes, the sensor listen to the other sensors but does not monitor the area.
C. Deciding State: the sensors monitor the area but will change there state to either active or idle state soon.
Step:-4. Each sensor knows its neighboring sensor and broadcast its current energy level and sensor id and then stays in deciding state with its maximum sensing range.
Step:-5. When sensor nodes are in a deciding state with range r, then they should change their state into: active and idle.
Step:-6. For each sensor a. In ILBPSH, the load balancing algorithm is used to keep as many sensors alive as possible and then let them die simultaneously. Active state with sensing range r, if region R which is not covered by another active or deciding sensors. Idle state when a sensor is overused compared to its neighbors or when a sensor decreases its range to zero. This process stops after all sensors make a decision.
b. In PEIPSH, attempts are made to minimize the energy consumption for low energy sensors and maximize energy consumption for higher energy sensors. Each sensor decides which sensor is head node of by using the maximal lifetime of all the sensor of its neighbors. After building this conclusion, each sensor decides to become active with range r (r ≤ maximum sensing range) or decides to sleep. This process stops after all sensors make a decision.
Step:-7. The decision of all the states to be active or idle state is decided by sensors and each sensor will stay in that state for a specified period of time called, shuffle time, or upto that time when head sensor consumes its energy supply and is going to die. Here wakeup call is used for alerting all sensors and then they change their state back to the deciding state with their maximum sensing range and repeat the process from step 6.
Step:-8. This simulation is repeated until energy level of all sensors reaches zero.
Step:-9. Then, the process finishes and the lifetime of the wireless sensor networks is printed out.
5.2. Simulation Setup
For the simulation purpose, we created a static network of sensors in a 100m x 100m area. The adjustable parameters are: S, number of sensor nodes. We vary this from 40 to 200. There is one base station at location (75, 75). P sensing ranges r1, r2,...,rP. We vary P this from 1 to 6 and each sensor P = 6 sensing ranges with values 10m,20m,30m,40m,50 and 60m. The initial energy of each sensor node is 0.5 J. In this paper, the energy model is defined as the networks of all nodes having different initial energy and sensor nodes are equipped with more energy resources than the normal sensor nodes. Let mbe the fraction of the total number of nodes n, and mois the percentage of the total number of nodes m which are equipped with β times more energy than the normal nodes, we call these nodes as super nodes. The rest nodes are equipped with α times more energy than the normal nodes; we refer to these nodes as advanced nodes and remaining as normal nodes.
We suppose that all nodes are distributed uniformly over the sensor region R. Suppose E0 is the initial energy of each normal node. The energy of each super node is thenand each advanced node is then . The total initial Energy is
E0 +n*m (1-m0)*E0*(1+) +n*m*m0*E0*(1+) (3)
6. Results and Discussions
In this section, we evaluate the performance of PEIPSH and ILBPSH algorithms. We simulate random deployed network located in a 100m×100m area. We implement a new model in the algorithms in heterogeneous form and all nodes initially have the same energy. The figures indicate the lifetime for sensor nodes (Advance, Super, Normal nodes) in case of adjustable sensing ranges. We have considered a base station at the position (50, 50) and the number of sensors have been varied between 40 and 200 with an increment of 20. The largest sensing of range 60 meters has been taken in all cases. We have compared the network lifetime for six adjustable sensing ranges which are 10, 20, 30, 40, 50 and 60 meters.
6.1. Power Efficient Information Gathering Protocol for Adjustable Range Sensing with Heterogeneity (PEIPSH)
The following paragraphs discuss the simulation results for PEIPSH and their lifetime comparisons with different adjustable sensing ranges have been reported.
Case I: α =2, β =1, m=0.2, m0=0.5
Figure 4 indicates the lifetime for sensor nodes in case of heterogeneous nodes and different adjustable sensing ranges. It has been observed that when the sensing range is varied from 1 to 4 there is significant increment in lifetime of the network while for other sensing ranges the change is very small. It has been shown that for 200 numbers of sensors the lifetime obtained in case of PEIPSH is [18.50, 28.22, 34.51, 38.79, 41.41, and 42.06] respectively in case of sensing ranges of 1 to 6.
Case II: α =1, β =2, m=0.2, m0=0.5
Figure 5 points out the lifetime the of sensor networks in case of heterogeneous nodes and different adjustable sensing ranges. It has been concluded that when the sensing range is varied from 1 to 4 there is significant improvement in lifetime of the network while for other sensing range the change is very small. It has been shown that for 200 numbers of sensors the lifetime obtained in case of PEIPSH is [17.14, 26.15, 31.98, 35.94, 38.38, and 38.97] respectively in case of sensing ranges of 1 to 6.
6.2. Information Gathering Load Balancing Protocol for Adjustable Range Sensing with Heterogeneity (ILBPSH)
The following paragraphs discuss the simulation results for ILBPSH and their lifetime comparisons with different adjustable sensing ranges have been reported.
Case I: α =2, β =1, m=0.2, m0=0.5
Figure 6 reports the lifetime of sensor networks in case of heterogeneous sensor nodes and different adjustable sensing ranges. It has been observed that when the sensing range is varied from 1 to 4 there is significant improvement in lifetime of the wireless network while for other sensing range the change is very small. It has been shown that for 200 numbers of sensors the lifetime obtained in case of ILBPSH is [19.86, 29.03, 35.35, 39.65, 42.13, and 42.90] respectively in case of sensing ranges of 1 to 6.
Case II: α =1, β =2, m=0.2, m0=0.5
Figure 7 indicates the lifetime for sensor nodes in case of heterogeneous nodes and different adjustable sensing ranges. It has been concluded that when the sensing range is varied from 1 to 4 there is significant increment in lifetime of the network while for other sensing range the change is very small. It has been shown that for 200 numbers of sensors the lifetime obtained in case of ILBPSH is [18.40, 26.90, 32.76, 36.75, 39.04, and 39.75] respectively in case of sensing ranges of 1 to 6.
In this paper, we have proposed two energy-efficient centralized algorithms for increasing the lifetime of wireless sensor networks with adjustable sensing ranges. Our approach is schedule and energy based: Scheduling sensor nodes to alternate between sleep and active mode is an important method to conserve energy resources and head node are randomly selected based on there residual energy and distance from the base-station. Such mechanisms efficiently organize or schedule the sensor activity and have a direct impact on prolonging the network lifetime. The proposed algorithms PEIPSH and ILBPSH work well in increasing the network lifetime and decreasing the energy consumption to transmit data in simulation. In all the Cases for PEIPSH and ILBPSH protocols, the lifetime of sensor networks shows an increment from [18 to 42; 17 to 38; 19 to 45] and [19 to 45; 18 to 39; 21 to 46] hours for sensing range 1-6 respectively.